Schema Evolution publication categorizer

This paper presents our ongoing effort on developing a principled
methodology for automatic ontology mapping based on BayesOWL, a probabilistic
framework we developed for modeling uncertainty in semantic web. In
this approach, the source and target ontologies are first translated into Bayesian
networks (BN); the concept mapping between the two ontologies are treated as
evidential reasoning between the two translated BNs. Probabilities needed for
constructing conditional probability tables (CPT) during translation and for
measuring semantic similarity during mapping are learned using text classification
techniques where each concept in an ontology is associated with a set of
semantically relevant text documents, which are obtained by ontology guided
web mining. The basic ideas of this approach are validated by positive results
from computer experiments on two small real-world ontologies.